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Most studies aiming to construct reference or standard charts use a cross-sectional design, collecting one measurement per participant. Reference or standard charts can also be constructed using a longitudinal design, collecting multiple measurements per participant. The choice of appropriate statistical methodology is important as inaccurate centiles resulting from inferior methods can lead to incorrect judgements about fetal or newborn size, resulting in suboptimal clinical care. Reference or standard centiles should ideally provide the best fit to the data, change smoothly with age (eg, gestational age), use as simple a statistical model as possible without compromising model fit, and allow the computation of Z-scores from centiles to simplify assessment of individuals and enable comparison with different populations. Significance testing and goodness-of-fit statistics are usually used to discriminate between models. However, these methods tend not to be useful when examining large data sets as very small differences are statistically significant even if the models are indistinguishable on actual centile plots. Choosing the best model from amongst many is therefore not trivial. Model choice should not be based on statistical considerations (or tests) alone as sometimes the best model may not necessarily offer the best fit to the raw data across gestational age. In this paper, we describe the most commonly applied methodologies available for the construction of age-specific reference or standard centiles for cross-sectional and longitudinal data: Fractional polynomial regression, LMS, LMST, LMSP, and multilevel regression methods. For illustration, we used data from the INTERGROWTH-21st Project, ie, newborn weight (cross-sectional) and fetal head circumference (longitudinal) data as examples.

Original publication




Journal article


Stat med

Publication Date





3507 - 3526


cross-sectional, human growth, longitudinal, statistical methodology, Cross-Sectional Studies, Female, Gestational Age, Growth Charts, Humans, Infant, Newborn, Longitudinal Studies, Male, Reference Values, Regression Analysis, Sex Distribution